Notebooks for Higher Education
The notebooks in this section have been designed for a higher education audience. You can see information about the notebook and access it for use with Noteable.
GeoScience
Notebooks focused on the GeoScience Notebook.
Seismology Obspy Notebook
Explore the Obspy framework for seismic data. Utilise ipywidgets to make the tutorial interactive.
Ridgemap Notebook
Explores ridge_map
, a geospatial library accessing SRTM elevation data. Demonstrates plotting elevation data as ridge maps.
Rasterio & Matplotlib Notebook
Explores the rasterio
and matplotlib
libraries to visualize Ordnance Survey terrain digital terrain elevation models of the Lake District obtained from EDINA's Digimap service.
EarthPy Notebook
Explore the EarthPy library to visualise Ordnance Survey terrain of the Lake District obtained from EDINA's Digimap service to plot terrain maps with hillshading to make more 3-Dimensional.
Folium Notebook
Explore the Folium library to plot markers, polylines and other vector/raster/HTML visualisations on a base map.
Covid-19 Notebook
Explore the Folium library for making Choropleth maps through a step-by-step guide using Covid-19 data from John Hopkins University.
KeplerGl Notebook
Explore the KeplerGl interface embedded in a Jupyter map, to create map layers of the global population and GDP.
Language and Machine Learning
Notebooks focused on the Language and Machine Learning Notebook.
Classifications Notebook
Use the base Scipy stack of libraries to visusalise classification-K nearest neighbours (k-NN) and principal component analysis (PCA).
Clustering-k-means Notebook
Use the base Scipy stack of libraries to visualise Clustering K-means of generated data. Makes use of the bokeh library to make the plots interactive.
K-means Compression Notebook
Use the base Scipy stack of libraries to create image filters, making use of ipywidgets to make the tutorial interactive.
Regression Medical Notebook
Use the base Scipy stack of libraries to perform linear regression on a sample diabetes dataset. Make use of the bokeh library to make the plots interactive.
The R language
Notebooks focused on the R-language Notebook.
(Note that the RStudio notebook does not run .ipynb
Notebooks.)
Data Cleaning with R
Explore Data Cleaning and Exploratory Analysis (EDA) with R. Demonstrate a typical data cleaning process using a dataset on breast cancer, sample data included.